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Selective texture characterization using Gabor filters

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Date Issued:
1993
Summary:
The objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations focuses on the Gabor function and the biological analog that exists between it and the simple cells of the striate cortex. A simulation generates examples of the use of the Gabor filter as a line detector with synthetic data. Simulations are then presented using Gabor filters for real texture characterization. The Gabor filter spatial and spectral attributes are selectively chosen based on the information from a scale-space image in order to maximize resolution of the characterization process. A variation of probabilistic relaxation that exploits the Gabor filter spatial and spectral attributes is devised, and used to force a consensus of the filter responses for texture characterization. We then perform segmentation of the image using the concept of isolation of low energy states within an image. This iterative smoothing algorithm, operating as a Gabor filter post-processing stage, depends on a line processes discontinuity threshold. Selection of the discontinuity threshold is obtained from the modes of the histogram of the relaxed Gabor filter responses using probabilistic relaxation to detect the significant modes. We test our algorithm on simple synthetic and real textures, then use a more complex natural texture image to test the entire algorithm. Limitations on textural resolution are noted, as well as for the resolution of the image segmentation process.
Title: Selective texture characterization using Gabor filters.
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Name(s): Boutros, George.
Florida Atlantic University, Thesis advisor
Sudhakar, Raghavan, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1993
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 234 p.
Language(s): English
Summary: The objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations focuses on the Gabor function and the biological analog that exists between it and the simple cells of the striate cortex. A simulation generates examples of the use of the Gabor filter as a line detector with synthetic data. Simulations are then presented using Gabor filters for real texture characterization. The Gabor filter spatial and spectral attributes are selectively chosen based on the information from a scale-space image in order to maximize resolution of the characterization process. A variation of probabilistic relaxation that exploits the Gabor filter spatial and spectral attributes is devised, and used to force a consensus of the filter responses for texture characterization. We then perform segmentation of the image using the concept of isolation of low energy states within an image. This iterative smoothing algorithm, operating as a Gabor filter post-processing stage, depends on a line processes discontinuity threshold. Selection of the discontinuity threshold is obtained from the modes of the histogram of the relaxed Gabor filter responses using probabilistic relaxation to detect the significant modes. We test our algorithm on simple synthetic and real textures, then use a more complex natural texture image to test the entire algorithm. Limitations on textural resolution are noted, as well as for the resolution of the image segmentation process.
Identifier: 12342 (digitool), FADT12342 (IID), fau:9244 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1993.
Subject(s): Image processing--Digital techniques
Computer vision
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12342
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.